Electron flow matching for generative reaction mechanism prediction obeying conservation laws

Mass conservation is a fundamental principle in chemistry, servicing as a critical constraint for accurately modeling chemical reactions. Postulated by Antoine Lavoisier in the eighteenth century, it asserts that the total mass of reactants equals the total mass of products, forming the basis for stoichiometry and chemical equation balancing. Despite its simplicity and essentiality, many machine learning models trained on chemical reaction data do not inherently enforce mass conservation. In this work, we introduce a new modeling formulation for reaction outcome prediction that achieves exact conservation by modeling chemical reactivity as a generative and probabilistic process of electron redistribution. The task of reaction outcome prediction has become a popular target for supervised machine learning [1, 2]. While chemists typically conceptualize, visualize, and communicate understanding of chemical reactions through mechanistic arrow-pushing diagrams, most data-driven models bypass this formalism and focus solely on predicting the major product in an end-to-end manner.